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Deep Learning Architectures : A Mathematical Approach / / by Ovidiu Calin
| Deep Learning Architectures : A Mathematical Approach / / by Ovidiu Calin |
| Autore | Calin Ovidiu |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (XXX, 760 p. 213 illus., 35 illus. in color.) |
| Disciplina |
006.31
006.310151 |
| Collana | Springer Series in the Data Sciences |
| Soggetto topico |
Computer science—Mathematics
Computer mathematics Machine learning Mathematical Applications in Computer Science Machine Learning |
| ISBN | 3-030-36721-5 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introductory Problems -- Activation Functions -- Cost Functions -- Finding Minima Algorithms -- Abstract Neurons -- Neural Networks -- Approximation Theorems -- Learning with One-dimensional Inputs -- Universal Approximators -- Exact Learning -- Information Representation -- Information Capacity Assessment -- Output Manifolds -- Neuromanifolds -- Pooling -- Convolutional Networks -- Recurrent Neural Networks -- Classification -- Generative Models -- Stochastic Networks -- Hints and Solutions. . |
| Record Nr. | UNINA-9910484905703321 |
Calin Ovidiu
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| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Demystifying Deep Learning : An Introduction to the Mathematics of Neural Networks / / Douglas J. Santry
| Demystifying Deep Learning : An Introduction to the Mathematics of Neural Networks / / Douglas J. Santry |
| Autore | Santry Douglas J. |
| Edizione | [First edition.] |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : John Wiley & Sons, Inc., , [2024] |
| Descrizione fisica | 1 online resource (259 pages) |
| Disciplina | 006.310151 |
| Soggetto topico | Deep learning (Machine learning) |
| ISBN |
1-394-20563-5
1-394-20561-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Title Page -- Copyright -- Contents -- About the Author -- Acronyms -- Chapter 1 Introduction -- 1.1 AI/ML - Deep Learning? -- 1.2 A Brief History -- 1.3 The Genesis of Models -- 1.3.1 Rise of the Empirical Functions -- 1.3.2 The Biological Phenomenon and the Analogue -- 1.4 Numerical Computation - Computer Numbers Are Not ℝeal -- 1.4.1 The IEEE 754 Floating Point System -- 1.4.2 Numerical Coding Tip: Think in Floating Point -- 1.5 Summary -- 1.6 Projects -- Chapter 2 Deep Learning and Neural Networks -- 2.1 Feed‐Forward and Fully‐Connected Artificial Neural Networks -- 2.2 Computing Neuron State -- 2.2.1 Activation Functions -- 2.3 The Feed‐Forward ANN Expressed with Matrices -- 2.3.1 Neural Matrices: A Convenient Notation -- 2.4 Classification -- 2.4.1 Binary Classification -- 2.4.2 One‐Hot Encoding -- 2.4.3 The Softmax Layer -- 2.5 Summary -- 2.6 Projects -- Chapter 3 Training Neural Networks -- 3.1 Preparing the Training Set: Data Preprocessing -- 3.2 Weight Initialization -- 3.3 Training Outline -- 3.4 Least Squares: A Trivial Example -- 3.5 Backpropagation of Error for Regression -- 3.5.1 The Terminal Layer (Output) -- 3.5.2 Backpropagation: The Shallower Layers -- 3.5.3 The Complete Backpropagation Algorithm -- 3.5.4 A Word on the Rectified Linear Unit (ReLU) -- 3.6 Stochastic Sine -- 3.7 Verification of a Software Implementation -- 3.8 Summary -- 3.9 Projects -- Chapter 4 Training Classifiers -- 4.1 Backpropagation for Classifiers -- 4.1.1 Likelihood -- 4.1.2 Categorical Loss Functions -- 4.2 Computing the Derivative of the Loss -- 4.2.1 Initiate Backpropagation -- 4.3 Multilabel Classification -- 4.3.1 Binary Classification -- 4.3.2 Training A Multilabel Classifier ANN -- 4.4 Summary -- 4.5 Projects -- Chapter 5 Weight Update Strategies -- 5.1 Stochastic Gradient Descent -- 5.2 Weight Updates as Iteration and Convex Optimization.
5.2.1 Newton's Method for Optimization -- 5.3 RPROP+ -- 5.4 Momentum Methods -- 5.4.1 AdaGrad and RMSProp -- 5.4.2 ADAM -- 5.5 Levenberg-Marquard Optimization for Neural Networks -- 5.6 Summary -- 5.7 Projects -- Chapter 6 Convolutional Neural Networks -- 6.1 Motivation -- 6.2 Convolutions and Features -- 6.3 Filters -- 6.4 Pooling -- 6.5 Feature Layers -- 6.6 Training a CNN -- 6.6.1 Flatten and the Gradient -- 6.6.2 Pooling and the Gradient -- 6.6.3 Filters and the Gradient -- 6.7 Applications -- 6.8 Summary -- 6.9 Projects -- Chapter 7 Fixing the Fit -- 7.1 Quality of the Solution -- 7.2 Generalization Error -- 7.2.1 Bias -- 7.2.2 Variance -- 7.2.3 The Bias‐Variance Trade‐off -- 7.2.4 The Bias‐Variance Trade‐off in Context -- 7.2.5 The Test Set -- 7.3 Classification Performance -- 7.4 Regularization -- 7.4.1 Forward Pass During Training -- 7.4.2 Forward Pass During Normal Inference -- 7.4.3 Backpropagation of Error -- 7.5 Advanced Normalization -- 7.5.1 Batch Normalization -- 7.5.2 Layer Normalization -- 7.6 Summary -- 7.7 Projects -- Chapter 8 Design Principles for a Deep Learning Training Library -- 8.1 Computer Languages -- 8.2 The Matrix: Crux of a Library Implementation -- 8.2.1 Memory Access and Modern CPU Architectures -- 8.2.2 Designing Matrix Computations -- 8.2.2.1 Convolutions as Matrices -- 8.3 The Framework -- 8.4 Summary -- 8.5 Projects -- Chapter 9 Vistas -- 9.1 The Limits of ANN Learning Capacity -- 9.2 Generative Adversarial Networks -- 9.2.1 GAN Architecture -- 9.2.2 The GAN Loss Function -- 9.3 Reinforcement Learning -- 9.3.1 The Elements of Reinforcement Learning -- 9.3.2 A Trivial RL Training Algorithm -- 9.4 Natural Language Processing Transformed -- 9.4.1 The Challenges of Natural Language -- 9.4.2 Word Embeddings -- 9.4.3 Attention -- 9.4.4 Transformer Blocks -- 9.4.5 Multi‐Head Attention -- 9.4.6 Transformer Applications. 9.5 Neural Turing Machines -- 9.6 Summary -- 9.7 Projects -- Appendix A Mathematical Review -- A.1 Linear Algebra -- A.1.1 Vectors -- A.1.2 Matrices -- A.1.3 Matrix Properties -- A.1.4 Linear Independence -- A.1.5 The QR Decomposition -- A.1.6 Least Squares -- A.1.7 Eigenvalues and Eigenvectors -- A.1.8 Hadamard Operations -- A.2 Basic Calculus -- A.2.1 The Product Rule -- A.2.2 The Chain Rule -- A.2.3 Multivariable Functions -- A.2.4 Taylor Series -- A.3 Advanced Matrices -- A.4 Probability -- Glossary -- References -- Index -- EULA. |
| Record Nr. | UNINA-9910830539803321 |
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Santry Douglas J.
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| Hoboken, New Jersey : , : John Wiley & Sons, Inc., , [2024] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Mathematical Theories of Machine Learning - Theory and Applications / / by Bin Shi, S. S. Iyengar
| Mathematical Theories of Machine Learning - Theory and Applications / / by Bin Shi, S. S. Iyengar |
| Autore | Shi Bin |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (XXI, 133 p. 25 illus., 24 illus. in color.) |
| Disciplina |
621.382
006.310151 |
| Soggetto topico |
Electrical engineering
Computational intelligence Data mining Information storage and retrieval Big data Communications Engineering, Networks Computational Intelligence Data Mining and Knowledge Discovery Information Storage and Retrieval Big Data/Analytics |
| ISBN | 3-030-17076-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1. Introduction -- Chapter 2. General Framework of Mathematics -- Chapter 3. Problem Formulation -- Chapter 4. Development of Novel Techniques of CoCoSSC Method -- Chapter 5. Further Discussions of the Proposed Method -- Chapter 6. Related Work on Geometry of Non-Convex Programs -- Chapter 7. Gradient Descent Converges to Minimizers -- Chapter 8. A Conservation Law Method Based on Optimization -- Chapter 9. Improved Sample Complexity in Sparse Subspace Clustering with Noisy and Missing Observations -- Chapter 10. Online Discovery for Stable and Grouping Causalities in Multi-Variate Time Series -- Chapter 11. Conclusion. |
| Record Nr. | UNINA-9910366589703321 |
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Shi Bin
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| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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